nifty_core.py 103 KB
Newer Older
1
2
# NIFTY (Numerical Information Field Theory) has been developed at the
# Max-Planck-Institute for Astrophysics.
Marco Selig's avatar
Marco Selig committed
3
##
4
# Copyright (C) 2013 Max-Planck-Society
Marco Selig's avatar
Marco Selig committed
5
##
6
7
# Author: Marco Selig
# Project homepage: <http://www.mpa-garching.mpg.de/ift/nifty/>
Marco Selig's avatar
Marco Selig committed
8
##
9
10
11
12
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
Marco Selig's avatar
Marco Selig committed
13
##
14
15
16
17
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU General Public License for more details.
Marco Selig's avatar
Marco Selig committed
18
##
19
20
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
Marco Selig's avatar
Marco Selig committed
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44

"""
    ..                  __   ____   __
    ..                /__/ /   _/ /  /_
    ..      __ ___    __  /  /_  /   _/  __   __
    ..    /   _   | /  / /   _/ /  /   /  / /  /
    ..   /  / /  / /  / /  /   /  /_  /  /_/  /
    ..  /__/ /__/ /__/ /__/    \___/  \___   /  core
    ..                               /______/

    .. The NIFTY project homepage is http://www.mpa-garching.mpg.de/ift/nifty/

    NIFTY [#]_, "Numerical Information Field Theory", is a versatile
    library designed to enable the development of signal inference algorithms
    that operate regardless of the underlying spatial grid and its resolution.
    Its object-oriented framework is written in Python, although it accesses
    libraries written in Cython, C++, and C for efficiency.

    NIFTY offers a toolkit that abstracts discretized representations of
    continuous spaces, fields in these spaces, and operators acting on fields
    into classes. Thereby, the correct normalization of operations on fields is
    taken care of automatically without concerning the user. This allows for an
    abstract formulation and programming of inference algorithms, including
    those derived within information field theory. Thus, NIFTY permits its user
Marco Selig's avatar
Marco Selig committed
45
    to rapidly prototype algorithms in 1D and then apply the developed code in
Marco Selig's avatar
Marco Selig committed
46
47
48
49
50
    higher-dimensional settings of real world problems. The set of spaces on
    which NIFTY operates comprises point sets, n-dimensional regular grids,
    spherical spaces, their harmonic counterparts, and product spaces
    constructed as combinations of those.

51
52
53
54
55
56
57
    References
    ----------
    .. [#] Selig et al., "NIFTY -- Numerical Information Field Theory --
        a versatile Python library for signal inference",
        `A&A, vol. 554, id. A26 <http://dx.doi.org/10.1051/0004-6361/201321236>`_,
        2013; `arXiv:1301.4499 <http://www.arxiv.org/abs/1301.4499>`_

Marco Selig's avatar
Marco Selig committed
58
59
60
61
62
63
    Class & Feature Overview
    ------------------------
    The NIFTY library features three main classes: **spaces** that represent
    certain grids, **fields** that are defined on spaces, and **operators**
    that apply to fields.

64
65
    .. Overview of all (core) classes:
    ..
Marco Selig's avatar
Marco Selig committed
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
    .. - switch
    .. - notification
    .. - _about
    .. - random
    .. - space
    ..     - point_space
    ..     - rg_space
    ..     - lm_space
    ..     - gl_space
    ..     - hp_space
    ..     - nested_space
    .. - field
    .. - operator
    ..     - diagonal_operator
    ..         - power_operator
    ..     - projection_operator
    ..     - vecvec_operator
    ..     - response_operator
    .. - probing
    ..     - trace_probing
    ..     - diagonal_probing

88
89
    Overview of the main classes and functions:

Marco Selig's avatar
Marco Selig committed
90
91
    .. automodule:: nifty

92
93
94
95
96
97
98
99
100
101
102
103
104
105
    - :py:class:`space`
        - :py:class:`point_space`
        - :py:class:`rg_space`
        - :py:class:`lm_space`
        - :py:class:`gl_space`
        - :py:class:`hp_space`
        - :py:class:`nested_space`
    - :py:class:`field`
    - :py:class:`operator`
        - :py:class:`diagonal_operator`
            - :py:class:`power_operator`
        - :py:class:`projection_operator`
        - :py:class:`vecvec_operator`
        - :py:class:`response_operator`
Marco Selig's avatar
Marco Selig committed
106

107
        .. currentmodule:: nifty.nifty_tools
Marco Selig's avatar
Marco Selig committed
108

109
110
        - :py:class:`invertible_operator`
        - :py:class:`propagator_operator`
Marco Selig's avatar
Marco Selig committed
111

112
        .. currentmodule:: nifty.nifty_explicit
Marco Selig's avatar
Marco Selig committed
113

114
        - :py:class:`explicit_operator`
Marco Selig's avatar
Marco Selig committed
115

116
    .. automodule:: nifty
Marco Selig's avatar
Marco Selig committed
117

118
119
120
    - :py:class:`probing`
        - :py:class:`trace_probing`
        - :py:class:`diagonal_probing`
Marco Selig's avatar
Marco Selig committed
121

122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
        .. currentmodule:: nifty.nifty_explicit

        - :py:class:`explicit_probing`

    .. currentmodule:: nifty.nifty_tools

    - :py:class:`conjugate_gradient`
    - :py:class:`steepest_descent`

    .. currentmodule:: nifty.nifty_explicit

    - :py:func:`explicify`

    .. currentmodule:: nifty.nifty_power

    - :py:func:`weight_power`,
      :py:func:`smooth_power`,
      :py:func:`infer_power`,
      :py:func:`interpolate_power`
Marco Selig's avatar
Marco Selig committed
141
142
143
144

"""
from __future__ import division
import numpy as np
Marco Selig's avatar
Marco Selig committed
145
import pylab as pl
146

147
148
149
from d2o import distributed_data_object,\
                STRATEGIES as DISTRIBUTION_STRATEGIES

150
from nifty_paradict import space_paradict,\
151
    point_space_paradict
Ultimanet's avatar
Ultimanet committed
152

153
154
155
from nifty.config import about,\
                         nifty_configuration as gc,\
                         dependency_injector as gdi
156

Ultimanet's avatar
Ultimanet committed
157
from nifty_random import random
158
import nifty.nifty_utilities as utilities
Marco Selig's avatar
Marco Selig committed
159

160
POINT_DISTRIBUTION_STRATEGIES = DISTRIBUTION_STRATEGIES['global']
Marco Selig's avatar
Marco Selig committed
161

Ultimanet's avatar
Ultimanet committed
162
163

class space(object):
Marco Selig's avatar
Marco Selig committed
164
    """
Ultimanet's avatar
Ultimanet committed
165
166
167
168
169
170
171
        ..     _______   ______    ____ __   _______   _______
        ..   /  _____/ /   _   | /   _   / /   ____/ /   __  /
        ..  /_____  / /  /_/  / /  /_/  / /  /____  /  /____/
        .. /_______/ /   ____/  \______|  \______/  \______/  class
        ..          /__/

        NIFTY base class for spaces and their discretizations.
Marco Selig's avatar
Marco Selig committed
172

Ultimanet's avatar
Ultimanet committed
173
174
175
        The base NIFTY space class is an abstract class from which other
        specific space subclasses, including those preimplemented in NIFTY
        (e.g. the regular grid class) must be derived.
Marco Selig's avatar
Marco Selig committed
176
177
178

        Parameters
        ----------
179
        dtype : numpy.dtype, *optional*
Ultimanet's avatar
Ultimanet committed
180
181
            Data type of the field values for a field defined on this space
            (default: numpy.float64).
182
        datamodel :
Marco Selig's avatar
Marco Selig committed
183
184
185

        See Also
        --------
Ultimanet's avatar
Ultimanet committed
186
187
188
189
190
191
192
193
        point_space :  A class for unstructured lists of numbers.
        rg_space : A class for regular cartesian grids in arbitrary dimensions.
        hp_space : A class for the HEALPix discretization of the sphere
            [#]_.
        gl_space : A class for the Gauss-Legendre discretization of the sphere
            [#]_.
        lm_space : A class for spherical harmonic components.
        nested_space : A class for product spaces.
Marco Selig's avatar
Marco Selig committed
194

Ultimanet's avatar
Ultimanet committed
195
196
197
198
199
200
201
202
        References
        ----------
        .. [#] K.M. Gorski et al., 2005, "HEALPix: A Framework for
               High-Resolution Discretization and Fast Analysis of Data
               Distributed on the Sphere", *ApJ* 622..759G.
        .. [#] M. Reinecke and D. Sverre Seljebotn, 2013, "Libsharp - spherical
               harmonic transforms revisited";
               `arXiv:1303.4945 <http://www.arxiv.org/abs/1303.4945>`_
Marco Selig's avatar
Marco Selig committed
203
204
205

        Attributes
        ----------
Ultimanet's avatar
Ultimanet committed
206
        para : {single object, list of objects}
207
208
209
            This is a freeform list of parameters that derivatives of the space
            class can use.
        dtype : numpy.dtype
Ultimanet's avatar
Ultimanet committed
210
211
212
213
214
215
216
            Data type of the field values for a field defined on this space.
        discrete : bool
            Whether the space is inherently discrete (true) or a discretization
            of a continuous space (false).
        vol : numpy.ndarray
            An array of pixel volumes, only one component if the pixels all
            have the same volume.
Marco Selig's avatar
Marco Selig committed
217
    """
218

Ultima's avatar
Ultima committed
219
    def __init__(self):
Marco Selig's avatar
Marco Selig committed
220
        """
Ultimanet's avatar
Ultimanet committed
221
            Sets the attributes for a space class instance.
Marco Selig's avatar
Marco Selig committed
222
223
224

            Parameters
            ----------
225
            dtype : numpy.dtype, *optional*
Ultimanet's avatar
Ultimanet committed
226
227
                Data type of the field values for a field defined on this space
                (default: numpy.float64).
228
            datamodel :
Marco Selig's avatar
Marco Selig committed
229

Ultimanet's avatar
Ultimanet committed
230
231
232
            Returns
            -------
            None
Marco Selig's avatar
Marco Selig committed
233
        """
234
        self.paradict = space_paradict()
235

Ultimanet's avatar
Ultimanet committed
236
237
238
    @property
    def para(self):
        return self.paradict['default']
239

Ultimanet's avatar
Ultimanet committed
240
241
242
    @para.setter
    def para(self, x):
        self.paradict['default'] = x
Marco Selig's avatar
Marco Selig committed
243

Ultima's avatar
Ultima committed
244
245
246
    def __hash__(self):
        return hash(())

247
    def _identifier(self):
Marco Selig's avatar
Marco Selig committed
248
        """
249
250
251
        _identiftier returns an object which contains all information needed
        to uniquely idetnify a space. It returns a (immutable) tuple which
        therefore can be compared.
252
        """
253
254
255
256
257
258
259
260
261
262
263
264
        return tuple(sorted(vars(self).items()))

    def __eq__(self, x):
        if isinstance(x, type(self)):
            return self._identifier() == x._identifier()
        else:
            return False

    def __ne__(self, x):
        return not self.__eq__(x)

    def __len__(self):
ultimanet's avatar
ultimanet committed
265
        return int(self.get_dim())
Marco Selig's avatar
Marco Selig committed
266

267
    def copy(self):
268
        return space(para=self.para,
269
                     dtype=self.dtype)
Marco Selig's avatar
Marco Selig committed
270

Ultimanet's avatar
Ultimanet committed
271
    def getitem(self, data, key):
272
        raise NotImplementedError(about._errors.cstring(
Ultimanet's avatar
Ultimanet committed
273
            "ERROR: no generic instance method 'getitem'."))
Marco Selig's avatar
Marco Selig committed
274

csongor's avatar
csongor committed
275
    def setitem(self, data, update, key):
276
        raise NotImplementedError(about._errors.cstring(
Ultimanet's avatar
Ultimanet committed
277
            "ERROR: no generic instance method 'getitem'."))
278

Ultimanet's avatar
Ultimanet committed
279
    def apply_scalar_function(self, x, function, inplace=False):
280
        raise NotImplementedError(about._errors.cstring(
Ultimanet's avatar
Ultimanet committed
281
            "ERROR: no generic instance method 'apply_scalar_function'."))
Marco Selig's avatar
Marco Selig committed
282

Ultimanet's avatar
Ultimanet committed
283
    def unary_operation(self, x, op=None):
284
        raise NotImplementedError(about._errors.cstring(
Ultimanet's avatar
Ultimanet committed
285
            "ERROR: no generic instance method 'unary_operation'."))
286

Ultimanet's avatar
Ultimanet committed
287
    def binary_operation(self, x, y, op=None):
288
        raise NotImplementedError(about._errors.cstring(
Ultimanet's avatar
Ultimanet committed
289
            "ERROR: no generic instance method 'binary_operation'."))
Marco Selig's avatar
Marco Selig committed
290

291
    def get_shape(self):
292
        raise NotImplementedError(about._errors.cstring(
Ultimanet's avatar
Ultimanet committed
293
            "ERROR: no generic instance method 'shape'."))
Marco Selig's avatar
Marco Selig committed
294

ultimanet's avatar
ultimanet committed
295
    def get_dim(self):
Marco Selig's avatar
Marco Selig committed
296
        """
Ultimanet's avatar
Ultimanet committed
297
            Computes the dimension of the space, i.e.\  the number of pixels.
Marco Selig's avatar
Marco Selig committed
298
299
300

            Parameters
            ----------
Ultimanet's avatar
Ultimanet committed
301
302
303
            split : bool, *optional*
                Whether to return the dimension split up, i.e. the numbers of
                pixels in each direction, or not (default: False).
Marco Selig's avatar
Marco Selig committed
304

Ultimanet's avatar
Ultimanet committed
305
306
307
308
            Returns
            -------
            dim : {int, numpy.ndarray}
                Dimension(s) of the space.
Marco Selig's avatar
Marco Selig committed
309
        """
310
        raise NotImplementedError(about._errors.cstring(
311
            "ERROR: no generic instance method 'dim'."))
Marco Selig's avatar
Marco Selig committed
312

313
    def get_dof(self):
Marco Selig's avatar
Marco Selig committed
314
        """
Ultimanet's avatar
Ultimanet committed
315
            Computes the number of degrees of freedom of the space.
Marco Selig's avatar
Marco Selig committed
316
317
318

            Returns
            -------
Ultimanet's avatar
Ultimanet committed
319
320
            dof : int
                Number of degrees of freedom of the space.
Marco Selig's avatar
Marco Selig committed
321
        """
322
        raise NotImplementedError(about._errors.cstring(
323
            "ERROR: no generic instance method 'dof'."))
Marco Selig's avatar
Marco Selig committed
324

325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
    def cast(self, x, verbose=False):
        """
            Computes valid field values from a given object, trying
            to translate the given data into a valid form. Thereby it is as
            benevolent as possible.

            Parameters
            ----------
            x : {float, numpy.ndarray, nifty.field}
                Object to be transformed into an array of valid field values.

            Returns
            -------
            x : numpy.ndarray, distributed_data_object
                Array containing the field values, which are compatible to the
                space.

            Other parameters
            ----------------
            verbose : bool, *optional*
                Whether the method should raise a warning if information is
                lost during casting (default: False).
        """
        raise NotImplementedError(about._errors.cstring(
            "ERROR: no generic instance method 'cast'."))
Marco Selig's avatar
Marco Selig committed
350

351
    # TODO: Move enforce power into power_indices class
352
    def enforce_power(self, spec, **kwargs):
Marco Selig's avatar
Marco Selig committed
353
        """
Ultimanet's avatar
Ultimanet committed
354
            Provides a valid power spectrum array from a given object.
Marco Selig's avatar
Marco Selig committed
355
356
357

            Parameters
            ----------
Ultimanet's avatar
Ultimanet committed
358
359
360
361
            spec : {scalar, list, numpy.ndarray, nifty.field, function}
                Fiducial power spectrum from which a valid power spectrum is to
                be calculated. Scalars are interpreted as constant power
                spectra.
Marco Selig's avatar
Marco Selig committed
362
363
364

            Returns
            -------
Ultimanet's avatar
Ultimanet committed
365
366
367
368
369
370
371
372
373
374
375
376
            spec : numpy.ndarray
                Valid power spectrum.

            Other parameters
            ----------------
            size : int, *optional*
                Number of bands the power spectrum shall have (default: None).
            kindex : numpy.ndarray, *optional*
                Scale of each band.
            codomain : nifty.space, *optional*
                A compatible codomain for power indexing (default: None).
            log : bool, *optional*
377
378
                Flag specifying if the spectral binning is performed on
                logarithmic
Ultimanet's avatar
Ultimanet committed
379
380
381
382
                scale or not; if set, the number of used bins is set
                automatically (if not given otherwise); by default no binning
                is done (default: None).
            nbin : integer, *optional*
383
384
                Number of used spectral bins; if given `log` is set to
                ``False``;
Ultimanet's avatar
Ultimanet committed
385
386
387
388
389
                integers below the minimum of 3 induce an automatic setting;
                by default no binning is done (default: None).
            binbounds : {list, array}, *optional*
                User specific inner boundaries of the bins, which are preferred
                over the above parameters; by default no binning is done
390
391
                (default: None).
            vmin : {scalar, list, ndarray, field}, *optional*
Ultimanet's avatar
Ultimanet committed
392
393
                Lower limit of the uniform distribution if ``random == "uni"``
                (default: 0).
Marco Selig's avatar
Marco Selig committed
394
395

        """
396
        raise NotImplementedError(about._errors.cstring(
397
            "ERROR: no generic instance method 'enforce_power'."))
Marco Selig's avatar
Marco Selig committed
398

399
    def check_codomain(self, codomain):
Marco Selig's avatar
Marco Selig committed
400
        """
401
            Checks whether a given codomain is compatible to the space or not.
Marco Selig's avatar
Marco Selig committed
402
403
404

            Parameters
            ----------
405
406
            codomain : nifty.space
                Space to be checked for compatibility.
Marco Selig's avatar
Marco Selig committed
407
408
409

            Returns
            -------
410
411
            check : bool
                Whether or not the given codomain is compatible to the space.
Marco Selig's avatar
Marco Selig committed
412
        """
Ultima's avatar
Ultima committed
413
414
415
416
417
        if codomain is None:
            return False
        else:
            raise NotImplementedError(about._errors.cstring(
                "ERROR: no generic instance method 'check_codomain'."))
Marco Selig's avatar
Marco Selig committed
418

419
    def get_codomain(self, **kwargs):
Marco Selig's avatar
Marco Selig committed
420
        """
421
422
423
            Generates a compatible codomain to which transformations are
            reasonable, usually either the position basis or the basis of
            harmonic eigenmodes.
Marco Selig's avatar
Marco Selig committed
424
425
426

            Parameters
            ----------
427
428
429
430
            coname : string, *optional*
                String specifying a desired codomain (default: None).
            cozerocenter : {bool, numpy.ndarray}, *optional*
                Whether or not the grid is zerocentered for each axis or not
Ultimanet's avatar
Ultimanet committed
431
                (default: None).
432
433
434
435
            conest : list, *optional*
                List of nested spaces of the codomain (default: None).
            coorder : list, *optional*
                Permutation of the list of nested spaces (default: None).
Marco Selig's avatar
Marco Selig committed
436
437
438

            Returns
            -------
439
440
            codomain : nifty.space
                A compatible codomain.
Ultimanet's avatar
Ultimanet committed
441
        """
442
        raise NotImplementedError(about._errors.cstring(
443
            "ERROR: no generic instance method 'get_codomain'."))
Marco Selig's avatar
Marco Selig committed
444

445
    def get_random_values(self, **kwargs):
Marco Selig's avatar
Marco Selig committed
446
        """
Ultimanet's avatar
Ultimanet committed
447
448
            Generates random field values according to the specifications given
            by the parameters.
Marco Selig's avatar
Marco Selig committed
449

Ultimanet's avatar
Ultimanet committed
450
451
452
453
454
455
456
            Returns
            -------
            x : numpy.ndarray
                Valid field values.

            Other parameters
            ----------------
Marco Selig's avatar
Marco Selig committed
457
            random : string, *optional*
Ultimanet's avatar
Ultimanet committed
458
459
460
                Specifies the probability distribution from which the random
                numbers are to be drawn.
                Supported distributions are:
Marco Selig's avatar
Marco Selig committed
461
462

                - "pm1" (uniform distribution over {+1,-1} or {+1,+i,-1,-i}
463
464
                - "gau" (normal distribution with zero-mean and a given
                    standard deviation or variance)
Marco Selig's avatar
Marco Selig committed
465
466
467
468
                - "syn" (synthesizes from a given power spectrum)
                - "uni" (uniform distribution over [vmin,vmax[)

                (default: None).
Ultimanet's avatar
Ultimanet committed
469
470
471
472
473
            dev : float, *optional*
                Standard deviation (default: 1).
            var : float, *optional*
                Variance, overriding `dev` if both are specified
                (default: 1).
474
475
            spec : {scalar, list, numpy.ndarray, nifty.field, function},
                    *optional*
Ultimanet's avatar
Ultimanet committed
476
                Power spectrum (default: 1).
477
478
479
480
            pindex : numpy.ndarray, *optional*
                Indexing array giving the power spectrum index of each band
                (default: None).
            kindex : numpy.ndarray, *optional*
Ultimanet's avatar
Ultimanet committed
481
                Scale of each band (default: None).
482
            codomain : nifty.space, *optional*
Ultimanet's avatar
Ultimanet committed
483
                A compatible codomain with power indices (default: None).
484
            log : bool, *optional*
485
486
                Flag specifying if the spectral binning is performed on
                logarithmic
487
488
489
490
                scale or not; if set, the number of used bins is set
                automatically (if not given otherwise); by default no binning
                is done (default: None).
            nbin : integer, *optional*
491
492
                Number of used spectral bins; if given `log` is set to
                ``False``;
493
494
495
496
497
                integers below the minimum of 3 induce an automatic setting;
                by default no binning is done (default: None).
            binbounds : {list, array}, *optional*
                User specific inner boundaries of the bins, which are preferred
                over the above parameters; by default no binning is done
498
499
                (default: None).
            vmin : {scalar, list, ndarray, field}, *optional*
500
501
                Lower limit of the uniform distribution if ``random == "uni"``
                (default: 0).
Ultimanet's avatar
Ultimanet committed
502
503
504
505
            vmin : float, *optional*
                Lower limit for a uniform distribution (default: 0).
            vmax : float, *optional*
                Upper limit for a uniform distribution (default: 1).
Marco Selig's avatar
Marco Selig committed
506
        """
507
508
        raise NotImplementedError(about._errors.cstring(
            "ERROR: no generic instance method 'get_random_values'."))
Marco Selig's avatar
Marco Selig committed
509

510
    def calc_weight(self, x, power=1):
Marco Selig's avatar
Marco Selig committed
511
        """
512
513
            Weights a given array of field values with the pixel volumes (not
            the meta volumes) to a given power.
Marco Selig's avatar
Marco Selig committed
514
515
516

            Parameters
            ----------
517
518
519
520
            x : numpy.ndarray
                Array to be weighted.
            power : float, *optional*
                Power of the pixel volumes to be used (default: 1).
Marco Selig's avatar
Marco Selig committed
521
522
523

            Returns
            -------
524
525
            y : numpy.ndarray
                Weighted array.
Marco Selig's avatar
Marco Selig committed
526
        """
527
        raise NotImplementedError(about._errors.cstring(
528
            "ERROR: no generic instance method 'calc_weight'."))
Marco Selig's avatar
Marco Selig committed
529

530
531
532
    def get_weight(self, power=1):
        raise NotImplementedError(about._errors.cstring(
            "ERROR: no generic instance method 'get_weight'."))
Marco Selig's avatar
Marco Selig committed
533

Ultima's avatar
Ultima committed
534
535
536
537
    def calc_norm(self, x, q):
        raise NotImplementedError(about._errors.cstring(
            "ERROR: no generic instance method 'norm'."))

538
    def calc_dot(self, x, y):
Marco Selig's avatar
Marco Selig committed
539
        """
540
541
            Computes the discrete inner product of two given arrays of field
            values.
Marco Selig's avatar
Marco Selig committed
542
543
544

            Parameters
            ----------
545
546
547
548
            x : numpy.ndarray
                First array
            y : numpy.ndarray
                Second array
Marco Selig's avatar
Marco Selig committed
549
550
551

            Returns
            -------
552
553
            dot : scalar
                Inner product of the two arrays.
Ultimanet's avatar
Ultimanet committed
554
        """
555
        raise NotImplementedError(about._errors.cstring(
556
            "ERROR: no generic instance method 'dot'."))
Marco Selig's avatar
Marco Selig committed
557

558
    def calc_transform(self, x, codomain=None, **kwargs):
Marco Selig's avatar
Marco Selig committed
559
        """
560
            Computes the transform of a given array of field values.
Marco Selig's avatar
Marco Selig committed
561

Ultimanet's avatar
Ultimanet committed
562
563
            Parameters
            ----------
564
565
566
567
568
            x : numpy.ndarray
                Array to be transformed.
            codomain : nifty.space, *optional*
                codomain space to which the transformation shall map
                (default: self).
Marco Selig's avatar
Marco Selig committed
569
570
571

            Returns
            -------
Ultimanet's avatar
Ultimanet committed
572
573
            Tx : numpy.ndarray
                Transformed array
574

Ultimanet's avatar
Ultimanet committed
575
576
577
578
            Other parameters
            ----------------
            iter : int, *optional*
                Number of iterations performed in specific transformations.
579
        """
580
581
        raise NotImplementedError(about._errors.cstring(
            "ERROR: no generic instance method 'calc_transform'."))
Marco Selig's avatar
Marco Selig committed
582

583
    def calc_smooth(self, x, sigma=0, **kwargs):
Marco Selig's avatar
Marco Selig committed
584
        """
Ultimanet's avatar
Ultimanet committed
585
586
            Smoothes an array of field values by convolution with a Gaussian
            kernel.
Marco Selig's avatar
Marco Selig committed
587
588
589

            Parameters
            ----------
Ultimanet's avatar
Ultimanet committed
590
591
592
593
594
            x : numpy.ndarray
                Array of field values to be smoothed.
            sigma : float, *optional*
                Standard deviation of the Gaussian kernel, specified in units
                of length in position space (default: 0).
Marco Selig's avatar
Marco Selig committed
595
596
597

            Returns
            -------
Ultimanet's avatar
Ultimanet committed
598
599
            Gx : numpy.ndarray
                Smoothed array.
Marco Selig's avatar
Marco Selig committed
600

Ultimanet's avatar
Ultimanet committed
601
602
603
604
            Other parameters
            ----------------
            iter : int, *optional*
                Number of iterations (default: 0).
Marco Selig's avatar
Marco Selig committed
605
        """
606
607
        raise NotImplementedError(about._errors.cstring(
            "ERROR: no generic instance method 'calc_smooth'."))
Marco Selig's avatar
Marco Selig committed
608

609
    def calc_power(self, x, **kwargs):
Marco Selig's avatar
Marco Selig committed
610
        """
Ultimanet's avatar
Ultimanet committed
611
            Computes the power of an array of field values.
Marco Selig's avatar
Marco Selig committed
612
613
614

            Parameters
            ----------
Ultimanet's avatar
Ultimanet committed
615
616
617
            x : numpy.ndarray
                Array containing the field values of which the power is to be
                calculated.
Marco Selig's avatar
Marco Selig committed
618
619
620
621

            Returns
            -------
            spec : numpy.ndarray
Ultimanet's avatar
Ultimanet committed
622
                Power contained in the input array.
Marco Selig's avatar
Marco Selig committed
623
624
625

            Other parameters
            ----------------
Ultimanet's avatar
Ultimanet committed
626
627
628
            pindex : numpy.ndarray, *optional*
                Indexing array assigning the input array components to
                components of the power spectrum (default: None).
629
            kindex : numpy.ndarray, *optional*
Ultimanet's avatar
Ultimanet committed
630
631
632
633
                Scale corresponding to each band in the power spectrum
                (default: None).
            rho : numpy.ndarray, *optional*
                Number of degrees of freedom per band (default: None).
634
635
636
            codomain : nifty.space, *optional*
                A compatible codomain for power indexing (default: None).
            log : bool, *optional*
637
638
                Flag specifying if the spectral binning is performed on
                logarithmic
639
640
641
642
                scale or not; if set, the number of used bins is set
                automatically (if not given otherwise); by default no binning
                is done (default: None).
            nbin : integer, *optional*
643
644
                Number of used spectral bins; if given `log` is set to
                ``False``;
645
646
647
648
649
                integers below the minimum of 3 induce an automatic setting;
                by default no binning is done (default: None).
            binbounds : {list, array}, *optional*
                User specific inner boundaries of the bins, which are preferred
                over the above parameters; by default no binning is done
650
651
                (default: None).
            vmin : {scalar, list, ndarray, field}, *optional*
652
653
                Lower limit of the uniform distribution if ``random == "uni"``
                (default: 0).
654

Marco Selig's avatar
Marco Selig committed
655
        """
656
657
        raise NotImplementedError(about._errors.cstring(
            "ERROR: no generic instance method 'calc_power'."))
Marco Selig's avatar
Marco Selig committed
658

659
660
661
662
663
664
665
    def calc_real_Q(self, x):
        raise NotImplementedError(about._errors.cstring(
            "ERROR: no generic instance method 'calc_real_Q'."))

    def calc_bincount(self, x, weights=None, minlength=None):
        raise NotImplementedError(about._errors.cstring(
            "ERROR: no generic instance method 'calc_bincount'."))
Marco Selig's avatar
Marco Selig committed
666

667
    def get_plot(self, x, **kwargs):
Marco Selig's avatar
Marco Selig committed
668
        """
Ultimanet's avatar
Ultimanet committed
669
670
            Creates a plot of field values according to the specifications
            given by the parameters.
671
672
673

            Parameters
            ----------
Ultimanet's avatar
Ultimanet committed
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
            x : numpy.ndarray
                Array containing the field values.

            Returns
            -------
            None

            Other parameters
            ----------------
            title : string, *optional*
                Title of the plot (default: "").
            vmin : float, *optional*
                Minimum value to be displayed (default: ``min(x)``).
            vmax : float, *optional*
                Maximum value to be displayed (default: ``max(x)``).
            power : bool, *optional*
                Whether to plot the power contained in the field or the field
                values themselves (default: False).
            unit : string, *optional*
                Unit of the field values (default: "").
            norm : string, *optional*
                Scaling of the field values before plotting (default: None).
            cmap : matplotlib.colors.LinearSegmentedColormap, *optional*
                Color map to be used for two-dimensional plots (default: None).
            cbar : bool, *optional*
                Whether to show the color bar or not (default: True).
            other : {single object, tuple of objects}, *optional*
                Object or tuple of objects to be added, where objects can be
                scalars, arrays, or fields (default: None).
            legend : bool, *optional*
                Whether to show the legend or not (default: False).
            mono : bool, *optional*
                Whether to plot the monopole or not (default: True).
            save : string, *optional*
                Valid file name where the figure is to be stored, by default
                the figure is not saved (default: False).
            error : {float, numpy.ndarray, nifty.field}, *optional*
                Object indicating some confidence interval to be plotted
                (default: None).
            kindex : numpy.ndarray, *optional*
                Scale corresponding to each band in the power spectrum
                (default: None).
            codomain : nifty.space, *optional*
                A compatible codomain for power indexing (default: None).
            log : bool, *optional*
719
720
                Flag specifying if the spectral binning is performed on
                logarithmic
721
722
723
                scale or not; if set, the number of used bins is set
                automatically (if not given otherwise); by default no binning
                is done (default: None).
Ultimanet's avatar
Ultimanet committed
724
            nbin : integer, *optional*
725
726
                Number of used spectral bins; if given `log` is set to
                ``False``;
727
                integers below the minimum of 3 induce an automatic setting;
728
                by default no binning is done (default: None).
Ultimanet's avatar
Ultimanet committed
729
            binbounds : {list, array}, *optional*
730
731
                User specific inner boundaries of the bins, which are preferred
                over the above parameters; by default no binning is done
732
733
                (default: None).
            vmin : {scalar, list, ndarray, field}, *optional*
Ultimanet's avatar
Ultimanet committed
734
735
736
737
                Lower limit of the uniform distribution if ``random == "uni"``
                (default: 0).
            iter : int, *optional*
                Number of iterations (default: 0).
Marco Selig's avatar
Marco Selig committed
738
739

        """
740
741
        raise NotImplementedError(about._errors.cstring(
            "ERROR: no generic instance method 'get_plot'."))
Marco Selig's avatar
Marco Selig committed
742

Ultimanet's avatar
Ultimanet committed
743
    def __repr__(self):
Ultima's avatar
Ultima committed
744
745
746
747
        string = ""
        string += str(type(self)) + "\n"
        string += "paradict: " + str(self.paradict) + "\n"
        return string
Marco Selig's avatar
Marco Selig committed
748

Ultimanet's avatar
Ultimanet committed
749
    def __str__(self):
Ultima's avatar
Ultima committed
750
        return self.__repr__()
Marco Selig's avatar
Marco Selig committed
751
752


Ultimanet's avatar
Ultimanet committed
753
class point_space(space):
Marco Selig's avatar
Marco Selig committed
754
    """
Ultimanet's avatar
Ultimanet committed
755
756
757
758
759
760
761
        ..                            __             __
        ..                          /__/           /  /_
        ..      ______    ______    __   __ ___   /   _/
        ..    /   _   | /   _   | /  / /   _   | /  /
        ..   /  /_/  / /  /_/  / /  / /  / /  / /  /_
        ..  /   ____/  \______/ /__/ /__/ /__/  \___/  space class
        .. /__/
Marco Selig's avatar
Marco Selig committed
762

Ultimanet's avatar
Ultimanet committed
763
        NIFTY subclass for unstructured spaces.
Marco Selig's avatar
Marco Selig committed
764

Ultimanet's avatar
Ultimanet committed
765
766
        Unstructured spaces are lists of values without any geometrical
        information.
Marco Selig's avatar
Marco Selig committed
767
768
769

        Parameters
        ----------
Ultimanet's avatar
Ultimanet committed
770
771
        num : int
            Number of points.
772
        dtype : numpy.dtype, *optional*
Ultimanet's avatar
Ultimanet committed
773
            Data type of the field values (default: None).
Marco Selig's avatar
Marco Selig committed
774

Ultimanet's avatar
Ultimanet committed
775
        Attributes
Marco Selig's avatar
Marco Selig committed
776
        ----------
Ultimanet's avatar
Ultimanet committed
777
778
        para : numpy.ndarray
            Array containing the number of points.
779
        dtype : numpy.dtype
Ultimanet's avatar
Ultimanet committed
780
781
782
783
784
785
            Data type of the field values.
        discrete : bool
            Parameter captioning the fact that a :py:class:`point_space` is
            always discrete.
        vol : numpy.ndarray
            Pixel volume of the :py:class:`point_space`, which is always 1.
Marco Selig's avatar
Marco Selig committed
786
    """
787

csongor's avatar
csongor committed
788
    def __init__(self, num, dtype=np.dtype('float')):
Ultimanet's avatar
Ultimanet committed
789
790
        """
            Sets the attributes for a point_space class instance.
Marco Selig's avatar
Marco Selig committed
791

Ultimanet's avatar
Ultimanet committed
792
793
794
795
            Parameters
            ----------
            num : int
                Number of points.
796
            dtype : numpy.dtype, *optional*
Ultimanet's avatar
Ultimanet committed
797
                Data type of the field values (default: numpy.float64).
Marco Selig's avatar
Marco Selig committed
798

Ultimanet's avatar
Ultimanet committed
799
800
801
802
            Returns
            -------
            None.
        """
Ultima's avatar
Ultima committed
803
        self._cache_dict = {'check_codomain': {}}
804
805
        self.paradict = point_space_paradict(num=num)

806
807
        # parse dtype
        dtype = np.dtype(dtype)
Ultima's avatar
Ultima committed
808
809
810
811
812
813
814
815
816
        if dtype not in [np.dtype('bool'),
                         np.dtype('int16'),
                         np.dtype('int32'),
                         np.dtype('int64'),
                         np.dtype('float32'),
                         np.dtype('float64'),
                         np.dtype('complex64'),
                         np.dtype('complex128')]:
            raise ValueError(about._errors.cstring(
817
                             "WARNING: incompatible dtype: " + str(dtype)))
Ultima's avatar
Ultima committed
818
        self.dtype = dtype
819

Ultimanet's avatar
Ultimanet committed
820
        self.discrete = True
Ultima's avatar
Ultima committed
821
#        self.harmonic = False
822
        self.distances = (np.float(1),)
Marco Selig's avatar
Marco Selig committed
823

Ultimanet's avatar
Ultimanet committed
824
825
826
827
    @property
    def para(self):
        temp = np.array([self.paradict['num']], dtype=int)
        return temp
828

Ultimanet's avatar
Ultimanet committed
829
830
    @para.setter
    def para(self, x):
Ultima's avatar
Ultima committed
831
        self.paradict['num'] = x[0]
832

Ultima's avatar
Ultima committed
833
834
835
836
    def __hash__(self):
        # Extract the identifying parts from the vars(self) dict.
        result_hash = 0
        for (key, item) in vars(self).items():
Ultima's avatar
Ultima committed
837
838
            if key in ['_cache_dict']:
                continue
Ultima's avatar
Ultima committed
839
840
841
            result_hash ^= item.__hash__() * hash(key)
        return result_hash

842
843
844
845
846
    def _identifier(self):
        # Extract the identifying parts from the vars(self) dict.
        temp = [(ii[0],
                 ((lambda x: x[1].__hash__() if x[0] == 'comm' else x)(ii)))
                for ii in vars(self).iteritems()
Ultima's avatar
Ultima committed
847
                if ii[0] not in ['_cache_dict']
848
849
850
851
                ]
        # Return the sorted identifiers as a tuple.
        return tuple(sorted(temp))

852
    def copy(self):
853
        return point_space(num=self.paradict['num'],
csongor's avatar
csongor committed
854
                           dtype=self.dtype)
855

Ultimanet's avatar
Ultimanet committed
856
857
    def getitem(self, data, key):
        return data[key]
Marco Selig's avatar
Marco Selig committed
858

Ultimanet's avatar
Ultimanet committed
859
    def setitem(self, data, update, key):
860
        data[key] = update
Marco Selig's avatar
Marco Selig committed
861

Ultimanet's avatar
Ultimanet committed
862
    def apply_scalar_function(self, x, function, inplace=False):
863
        return x.apply_scalar_function(function, inplace=inplace)
864

865
    def unary_operation(self, x, op='None', axis=None, **kwargs):
Ultimanet's avatar
Ultimanet committed
866
867
868
        """
        x must be a numpy array which is compatible with the space!
        Valid operations are
869

Ultimanet's avatar
Ultimanet committed
870
        """
871
872
873
874
875
        translation = {'pos': lambda y: getattr(y, '__pos__')(),
                       'neg': lambda y: getattr(y, '__neg__')(),
                       'abs': lambda y: getattr(y, '__abs__')(),
                       'real': lambda y: getattr(y, 'real'),
                       'imag': lambda y: getattr(y, 'imag'),
876
877
878
879
880
881
882
883
                       'nanmin': lambda y: getattr(y, 'nanmin')(axis=axis),
                       'amin': lambda y: getattr(y, 'amin')(axis=axis),
                       'nanmax': lambda y: getattr(y, 'nanmax')(axis=axis),
                       'amax': lambda y: getattr(y, 'amax')(axis=axis),
                       'median': lambda y: getattr(y, 'median')(axis=axis),
                       'mean': lambda y: getattr(y, 'mean')(axis=axis),
                       'std': lambda y: getattr(y, 'std')(axis=axis),
                       'var': lambda y: getattr(y, 'var')(axis=axis),
884
885
                       'argmin_nonflat': lambda y: getattr(y, 'argmin_nonflat')(
                           axis=axis),
csongor's avatar
csongor committed
886
                       'argmin': lambda y: getattr(y, 'argmin')(axis=axis),
887
888
                       'argmax_nonflat': lambda y: getattr(y, 'argmax_nonflat')(
                           axis=axis),
csongor's avatar
csongor committed
889
                       'argmax': lambda y: getattr(y, 'argmax')(axis=axis),
890
                       'conjugate': lambda y: getattr(y, 'conjugate')(),
891
892
                       'sum': lambda y: getattr(y, 'sum')(axis=axis),
                       'prod': lambda y: getattr(y, 'prod')(axis=axis),
893
894
895
896
897
898
899
                       'unique': lambda y: getattr(y, 'unique')(),
                       'copy': lambda y: getattr(y, 'copy')(),
                       'copy_empty': lambda y: getattr(y, 'copy_empty')(),
                       'isnan': lambda y: getattr(y, 'isnan')(),
                       'isinf': lambda y: getattr(y, 'isinf')(),
                       'isfinite': lambda y: getattr(y, 'isfinite')(),
                       'nan_to_num': lambda y: getattr(y, 'nan_to_num')(),
900
901
                       'all': lambda y: getattr(y, 'all')(axis=axis),
                       'any': lambda y: getattr(y, 'any')(axis=axis),
902
                       'None': lambda y: y}
Marco Selig's avatar
Marco Selig committed
903

904
905
        return translation[op](x, **kwargs)

Ultimanet's avatar
Ultimanet committed
906
    def binary_operation(self, x, y, op='None', cast=0):
907

Ultima's avatar
Ultima committed
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
        translation = {'add': lambda z: getattr(z, '__add__'),
                       'radd': lambda z: getattr(z, '__radd__'),
                       'iadd': lambda z: getattr(z, '__iadd__'),
                       'sub': lambda z: getattr(z, '__sub__'),
                       'rsub': lambda z: getattr(z, '__rsub__'),
                       'isub': lambda z: getattr(z, '__isub__'),
                       'mul': lambda z: getattr(z, '__mul__'),
                       'rmul': lambda z: getattr(z, '__rmul__'),
                       'imul': lambda z: getattr(z, '__imul__'),
                       'div': lambda z: getattr(z, '__div__'),
                       'rdiv': lambda z: getattr(z, '__rdiv__'),
                       'idiv': lambda z: getattr(z, '__idiv__'),
                       'pow': lambda z: getattr(z, '__pow__'),
                       'rpow': lambda z: getattr(z, '__rpow__'),
                       'ipow': lambda z: getattr(z, '__ipow__'),
                       'ne': lambda z: getattr(z, '__ne__'),
                       'lt': lambda z: getattr(z, '__lt__'),
                       'le': lambda z: getattr(z, '__le__'),
                       'eq': lambda z: getattr(z, '__eq__'),
                       'ge': lambda z: getattr(z, '__ge__'),
                       'gt': lambda z: getattr(z, '__gt__'),
                       'None': lambda z: lambda u: u}
930

Ultimanet's avatar
Ultimanet committed
931
932
933
        if (cast & 1) != 0:
            x = self.cast(x)
        if (cast & 2) != 0:
934
935
            y = self.cast(y)

Ultimanet's avatar
Ultimanet committed
936
        return translation[op](x)(y)
Marco Selig's avatar
Marco Selig committed
937

938
    def get_shape(self):
939
        return (self.paradict['num'],)
Marco Selig's avatar
Marco Selig committed
940

Ultima's avatar
Ultima committed
941
    def get_dim(self):
Ultimanet's avatar
Ultimanet committed
942
943
        """
            Computes the dimension of the space, i.e.\  the number of points.
Marco Selig's avatar
Marco Selig committed
944

Ultimanet's avatar
Ultimanet committed
945
946
947
948
949
            Parameters
            ----------
            split : bool, *optional*
                Whether to return the dimension as an array with one component
                or as a scalar (default: False).
Marco Selig's avatar
Marco Selig committed
950

Ultimanet's avatar
Ultimanet committed
951
952
953
954
955
            Returns
            -------
            dim : {int, numpy.ndarray}
                Dimension(s) of the space.
        """
Ultima's avatar
Ultima committed
956
        return np.prod(self.get_shape())
Marco Selig's avatar
Marco Selig committed
957

958
    def get_dof(self, split=False):
Ultimanet's avatar
Ultimanet committed
959
960
961
962
        """
            Computes the number of degrees of freedom of the space, i.e./  the
            number of points for real-valued fields and twice that number for
            complex-valued fields.
Marco Selig's avatar
Marco Selig committed
963

Ultimanet's avatar
Ultimanet committed
964
965
966
967
968
            Returns
            -------
            dof : int
                Number of degrees of freedom of the space.
        """
Ultima's avatar
Ultima committed
969
970
971
972
        if split:
            dof = self.get_shape()
            if issubclass(self.dtype.type, np.complexfloating):
                dof = tuple(np.array(dof)*2)
973
        else:
Ultima's avatar
Ultima committed
974
975
976
977
            dof = self.get_dim()
            if issubclass(self.dtype.type, np.complexfloating):
                dof = dof * 2
        return dof
978
979
980
981

    def get_vol(self, split=False):
        if split:
            return self.distances
Ultimanet's avatar
Ultimanet committed
982
        else:
983
            return np.prod(self.distances)
Marco Selig's avatar
Marco Selig committed
984

985
    def get_meta_volume(self, split=False):
Marco Selig's avatar
Marco Selig committed
986
        """
987
            Calculates the meta volumes.
Ultimanet's avatar
Ultimanet committed
988

989
990
991
992
993
            The meta volumes are the volumes associated with each component of
            a field, taking into account field components that are not
            explicitly included in the array of field values but are determined
            by symmetry conditions. In the case of an :py:class:`rg_space`, the
            meta volumes are simply the pixel volumes.
Marco Selig's avatar
Marco Selig committed
994
995
996

            Parameters
            ----------
997
998
999
            total : bool, *optional*
                Whether to return the total meta volume of the space or the
                individual ones of each pixel (default: False).
Marco Selig's avatar
Marco Selig committed
1000
1001
1002

            Returns
            -------
1003
1004
            mol : {numpy.ndarray, float}
                Meta volume of the pixels or the complete space.
Ultimanet's avatar
Ultimanet committed
1005
        """
1006
1007
1008
1009
1010
        if not split:
            return self.get_dim() * self.get_vol()
        else:
            mol = self.cast(1, dtype=np.dtype('float'))
            return self.calc_weight(mol, power=1)
1011

Ultima's avatar
Ultima committed
1012
    def cast(self, x=None, dtype=None, **kwargs):
1013
        return self._cast_to_d2o(x=x, dtype=dtype, **kwargs)
1014

Ultima's avatar
Ultima committed
1015
    def _cast_to_d2o(self, x, dtype=None, **kwargs):
1016
1017
        """
            Computes valid field values from a given object, trying
1018
1019
            to translate the given data into a valid form. Thereby it is as
            benevolent as possible.
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034

            Parameters
            ----------
            x : {float, numpy.ndarray, nifty.field}
                Object to be transformed into an array of valid field values.

            Returns
            -------
            x : numpy.ndarray, distributed_data_object
                Array containing the field values, which are compatible to the
                space.

            Other parameters
            ----------------
            verbose : bool, *optional*
1035
                Whether the method should raise a warning if information is
1036
1037
                lost during casting (default: False).
        """
1038
1039
        if dtype is not None:
            dtype = np.dtype(dtype)
1040
        if dtype is None:
1041
            dtype = self.dtype
1042

Ultima's avatar
Ultima committed
1043
        # Case 1: x is a distributed_data_object
1044
        if isinstance(x, distributed_data_object):
Ultima's avatar
Ultima committed
1045
1046
            to_copy = False

1047
            # Check the shape
1048
            if np.any(np.array(x.shape) != np.array(self.get_shape())):
1049
                # Check if at least the number of degrees of freedom is equal
1050
                if x.get_dim() == self.get_dim():
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
                    try:
                        temp = x.copy_empty(global_shape=self.get_shape())
                        temp.set_local_data(x.get_local_data(), copy=False)
                    except:
                        # If the number of dof is equal or 1, use np.reshape...
                        about.warnings.cflush(
                            "WARNING: Trying to reshape the data. This " +
                            "operation is expensive as it consolidates the " +
                            "full data!\n")
                        temp = x.get_full_data()
                        temp = np.reshape(temp, self.get_shape())
1062
                    # ... and cast again
Ultima's avatar
Ultima committed
1063
1064
1065
                    return self._cast_to_d2o(temp,
                                             dtype=dtype,
                                             **kwargs)
1066

1067
                else:
1068
1069
1070
                    raise ValueError(about._errors.cstring(
                        "ERROR: Data has incompatible shape!"))

1071
            # Check the dtype
1072
            if x.dtype != dtype:
Ultima's avatar
Ultima committed
1073
1074
1075
1076
1077
1078
                if x.dtype > dtype:
                    about.warnings.cflush(
                        "WARNING: Datatypes are of conflicting precision " +
                        "(own: " + str(dtype) + " <> foreign: " +
                        str(x.dtype) + ") and will be casted! Potential " +
                        "loss of precision!\n")
Ultima's avatar
Ultima committed
1079
1080
1081
                to_copy = True

            if to_copy:
csongor's avatar
csongor committed
1082
                temp = x.copy_empty(dtype=dtype)
1083
1084
                temp.set_data(to_key=(slice(None),),
                              data=x,
1085
                              from_key=(slice(None),))
1086
1087
                temp.hermitian = x.hermitian
                x = temp
1088

1089